This paper investigates the NETWORK LOCATION problem for single-server facilities that are subjected to congestion. On each NETWORK edge, customers are uniformly distributed, and their requests for service are assumed to be generated according to a Poisson process. A number of facilities are to be selected from a number of candidate sites, and a single server is located at each facility with exponentially distributed service times. Using queueing analysis, we develop a mixed integer mathematical model to minimize the total travel and the average waiting times for customers. For evaluation of the validity of the proposed model, a numerical example is solved and analyzed using GAMS software. In addition, since the proposed problem is NP-hard, two metaheuristic algorithms including a genetic algorithm and a simulated annealing algorithm are developed and applied for large-size problems.

Identification of the exact LOCATION of an exon in a DNA sequence is an important research area of bioinformatics. The main issues of the previous signal processing techniques are accuracy and robustness for the exact locating of exons. To address the mentioned issues, in this study, a method has been proposed based on deep learning. The proposed method includes a new preprocessing, a new mapping method, and a multi-scale modified and hybrid deep neural NETWORK. The proposed preprocessing method enriches the NETWORK to accept and encode genes at any length in a new mapping method. The proposed multi-scale deep neural NETWORK uses a combination of an embedding layer, a modified CNN, and an LSTM NETWORK. In this study, HMR195, BG570, and F56F11.4 datasets have been used to compare this work with previous studies. The accuracies of the proposed method have been 0.982, 0.966, and 0.965 on HMR195, BG570, and F56F11.4 databases, respectively. The results reveal the superiority and effectiveness of the proposed hybrid multi-scale CNN-LSTM NETWORK.

Nowadays, due to population increase and expansion of the transportation NETWORKs, the monitoring of this NETWORK, control, and prevention of accidents and crimes are very important. The main tasks of traffic patrols, like other emergency-security facilities, are the monitoring and prevention of crime as well as handling and fining in the case of committing crimes. Traffic patrols should be present in the scene and also in high traffic congestion, to reduce accidents and crimes. This paper presents a bi-objective and stochastic optimization model to design an emergency-security system. This NETWORK includes traffic patrol vehicles and manpower in patrol vehicles. The objective of the proposed model is to maximize the number of vehicles passing through patrols and minimize the costs according to different scenarios. To solve the model, the epsilonconstraint method is used which simultaneously determines the LOCATION of the patrols, alLOCATION of demand points to patrols, and determining the number of existing manpower in patrols. To evaluate and analyze the proposed model, a numerical example is used.

We consider a family of problems that combine NETWORK design and facility LOCATION. Such problems arise in many practical applications in di, erent , elds such as telecommunications, transportation NETWORKs, logistic, and energy supply NETWORKs. In facility LOCATION problems, we want to decide which facilities to open and how to assign clients to the open facilities so as to minimize the sum of the facility opening costs and client connection costs. These problems typically do not involve decisions concerning the routing of the clients' demands to the open facilities,once we decided on the set of open facilities, each client is served by the closest open facility. In NETWORK design problems, on the other hand, we generally want to design and dimension a minimum-cost routing NETWORK providing su, cient capacities to route all clients' demands to their destinations. These problems involve deciding on the routing of each client's demand. But, in contrast to facility LOCATION problems, demands' destinations are predetermined. In many modern day applications, however, all these decisions are interdependent and a, ect each other. Hence, they should be taken simultaneously. The aim of this work is to survey models, algorithmic approaches and methodologies concerning such combined NETWORK design facility LOCATION problems.

In this problem, there are two types of facilities and a NETWORK of nods. Facilities can only locate in nodes. There are three types of customers, two groups of which need only one type of services in a single trip, while the third group needs both types of services in a single trip. Customers in order to distance of facilities with a specified probability that related to distance, go to facilities and eliminate their needs. In order to competitors, the objective is maximize market capture for entering facilities. We use tabu search for solve this problem.